OpenRaiser/Intern-Atlas huggingface dataset:
A New Dynamic Optimal M2M RF interface setting in Relay Selection Algorithm (DORSA) for IoT Applications
Venue: arXiv cs.NI 2021
Year: 2021

Machine-to-Machine (M2M) communication is an important type of communication in the Internet-of-Things (IoT). How to send data in these high-density communications using relay selection can help improve the performance of this type of communication in various applications. In addition, the possibility of simultaneous use of different Radio Frequency (RF) interfaces helps to use the spectrum more efficiently. In this work, we try to further use machine communication RF equipment and improve the average data rate of networks in some applications such as the IoT, which have their own bandwidth requirements. Therefore, we provide an optimization algorithm for relay selection as well as the simultaneous and dynamic multiple M2M RF interface setting that is called Dynamic Optimal Relay Selection and RF interfaces Setting Algorithm (DORSA). The simulation results show that the average data rate of DORSA with three interfaces (DORSA_W-B-Z) can be improved up to 10% compared to the existing methods such as optimal direct transmission and relay selection algorithms with static RF interface setting.
Generating Significant Examples for Conceptual Schema Validation
Venue: arXiv cs.DB 2021
Year: 2021

This report bases itself on the idea of using concrete examples to verify conceptual schemas, and in particular cardinality constraints. When novice ORM modellers model domains, the selection of proper cardinality constraints for relationship types is quite often prone to errors. In this report we propose a mechanism for the generation of significant examples for selected subschemas. The generated examples are significant in the sense that they illustrate the possible combinations of instances that are allowed with respect to the cardinality constraints on the involved relationship types. In this report we firstly provide a brief informal discussion of the basic idea. Then we present a syntactic mechanism to select the subschema for which example instances are to be generated. This is followed by the actual example generation algorithm itself. We will also present, as a {\em spin-off}, an algorithm that allows us to detect possible flaws in the conceptual schema by calculating the number of instances that can be used to populate the types in the schema.
Aggregate Learning for Mixed Frequency Data
Venue: arXiv cs.LG 2021
Year: 2021

Large and acute economic shocks such as the 2007-2009 financial crisis and the current COVID-19 infections rapidly change the economic environment. In such a situation, the importance of real-time economic analysis using alternative datais emerging. Alternative data such as search query and location data are closer to real-time and richer than official statistics that are typically released once a month in an aggregated form. We take advantage of spatio-temporal granularity of alternative data and propose a mixed-FrequencyAggregate Learning (MF-AGL)model that predicts economic indicators for the smaller areas in real-time. We apply the model for the real-world problem; prediction of the number of job applicants which is closely related to the unemployment rates. We find that the proposed model predicts (i) the regional heterogeneity of the labor market condition and (ii) the rapidly changing economic status. The model can be applied to various tasks, especially economic analysis
AGSFCOS: Based on attention mechanism and Scale-Equalizing pyramid network of object detection
Venue: arXiv cs.CV 2021
Year: 2021

Recently, the anchor-free object detection model has shown great potential for accuracy and speed to exceed anchor-based object detection. Therefore, two issues are mainly studied in this article: (1) How to let the backbone network in the anchor-free object detection model learn feature extraction? (2) How to make better use of the feature pyramid network? In order to solve the above problems, Experiments show that our model has a certain improvement in accuracy compared with the current popular detection models on the COCO dataset, the designed attention mechanism module can capture contextual information well, improve detection accuracy, and use sepc network to help balance abstract and detailed information, and reduce the problem of semantic gap in the feature pyramid network. Whether it is anchor-based network model YOLOv3, Faster RCNN, or anchor-free network model Foveabox, FSAF, FCOS. Our optimal model can get 39.5% COCO AP under the background of ResNet50.
A Dispersed Federated Learning Framework for 6G-Enabled Autonomous Driving Cars
Venue: arXiv cs.NI 2021
Year: 2021

Sixth-Generation (6G)-based Internet of Everything applications (e.g. autonomous driving cars) have witnessed a remarkable interest. Autonomous driving cars using federated learning (FL) has the ability to enable different smart services. Although FL implements distributed machine learning model training without the requirement to move the data of devices to a centralized server, it its own implementation challenges such as robustness, centralized server security, communication resources constraints, and privacy leakage due to the capability of a malicious aggregation server to infer sensitive information of end-devices. To address the aforementioned limitations, a dispersed federated learning (DFL) framework for autonomous driving cars is proposed to offer robust, communication resource-efficient, and privacy-aware learning. A mixed-integer non-linear (MINLP) optimization problem is formulated to jointly minimize the loss in federated learning model accuracy due to packet errors and transmission latency. Due to the NP-hard and non-convex nature of the formulated MINLP problem, we propose the Block Successive Upper-bound Minimization (BSUM) based solution. Furthermore, the performance comparison of the proposed scheme with three baseline schemes has been carried out. Extensive numerical results are provided to show the validity of the proposed BSUM-based scheme.
A Connected Component Labelling algorithm for multi-pixel per clock cycle video stream
Venue: arXiv cs.CV 2021
Year: 2021

This work describes the hardware implementation of a connected component labelling (CCL) module in reprogammable logic. The main novelty of the design is the "full", i.e. without any simplifications, support of a 4 pixel per clock format (4 ppc) and real-time processing of a 4K/UltraHD video stream (3840 x 2160 pixels) at 60 frames per second. To achieve this, a special labelling method was designed and a functionality that stops the input data stream in order to process pixel groups which require writing more than one merger into the equivalence table. The proposed module was verified in simulation and in hardware on the Xilinx Zynq Ultrascale+ MPSoC chip on the ZCU104 evaluation board.
Towards Target-dependent Sentiment Classification in News Articles
Venue: arXiv cs.CL 2021
Year: 2021

Extensive research on target-dependent sentiment classification (TSC) has led to strong classification performances in domains where authors tend to explicitly express sentiment about specific entities or topics, such as in reviews or on social media. We investigate TSC in news articles, a much less researched domain, despite the importance of news as an essential information source in individual and societal decision making. This article introduces NewsTSC, a manually annotated dataset to explore TSC on news articles. Investigating characteristics of sentiment in news and contrasting them to popular TSC domains, we find that sentiment in the news is expressed less explicitly, is more dependent on context and readership, and requires a greater degree of interpretation. In an extensive evaluation, we find that the state of the art in TSC performs worse on news articles than on other domains (average recall AvgRec = 69.8 on NewsTSC compared to AvgRev = [75.6, 82.2] on established TSC datasets). Reasons include incorrectly resolved relation of target and sentiment-bearing phrases and off-context dependence. As a major improvement over previous news TSC, we find that BERT's natural language understanding capabilities capture the less explicit sentiment used in news articles.
An Empirical Study of Vehicle Re-Identification on the AI City Challenge
Venue: arXiv cs.CV 2021
Year: 2021

This paper introduces our solution for the Track2 in AI City Challenge 2021 (AICITY21). The Track2 is a vehicle re-identification (ReID) task with both the real-world data and synthetic data. We mainly focus on four points, i.e. training data, unsupervised domain-adaptive (UDA) training, post-processing, model ensembling in this challenge. (1) Both cropping training data and using synthetic data can help the model learn more discriminative features. (2) Since there is a new scenario in the test set that dose not appear in the training set, UDA methods perform well in the challenge. (3) Post-processing techniques including re-ranking, image-to-track retrieval, inter-camera fusion, etc, significantly improve final performance. (4) We ensemble CNN-based models and transformer-based models which provide different representation diversity. With aforementioned techniques, our method finally achieves 0.7445 mAP score, yielding the first place in the competition. Codes are available at https://github.com/michuanhaohao/AICITY2021_Track2_DMT.
Enhancement Programming Skills and Transforming Knowledge of Programming through Neuroeducation Approaches
Venue: arXiv cs.CY 2021
Year: 2021

Programming digital devices and developing software is an important professional qualification, which contributes to employment opportunities. Despite this fact, there is a remarkable shortage in suitable human resources. In this context, research studies focus on issues of programming didactic, teaching models, programming paradigms, which are meant to enhance and optimize programmers' skills. Recent development of brain imaging techniques such as electroencephalography and the functional magnetic resonance imaging, have provided additional opportunity for neuroscientists to explore the functional organization of the human brain. With the use of these techniques, this research is an approach to supporting learning in the field of learning and teaching computer programming. On one hand, there is an attempt to connect theoretical neurosciences with cognitive science; on the other hand, the obtained research data will contribute to the identification of practices that can be applied to formal and informal programming education.
Quantitative Physical Ergonomics Assessment of Teleoperation Interfaces
Venue: arXiv cs.HC 2021
Year: 2021

Human factors and ergonomics are the essential constituents of teleoperation interfaces, which can significantly affect the human operator's performance. Thus, a quantitative evaluation of these elements and the ability to establish reliable comparison bases for different teleoperation interfaces are the keys to select the most suitable one for a particular application. However, most of the works on teleoperation have so far focused on the stability analysis and the transparency improvement of these systems, and do not cover the important usability aspects. In this work, we propose a foundation to build a general framework for the analysis of human factors and ergonomics in employing diverse teleoperation interfaces. The proposed framework will go beyond the traditional subjective analyses of usability by complementing it with online measurements of the human body configurations. As a result, multiple quantitative metrics such as joints' usage, range of motion comfort, center of mass divergence, and posture comfort are introduced. To demonstrate the potential of the proposed framework, two different teleoperation interfaces are considered, and real-world experiments with eleven participants performing a simulated industrial remote pick-and-place task are conducted. The quantitative results of this analysis are provided, and compared with subjective questionnaires, illustrating the effectiveness of the proposed framework.
Uncoded Binary Signaling through Modulo AWGN Channel
Venue: arXiv cs.IT 2021
Year: 2021

Modulo-wrapping receivers have attracted interest in several areas of digital communications, including precoding and lattice coding. The asymptotic capacity and error performance of the modulo AWGN channel have been well established. However, due to underlying assumptions of the asymptotic analyses, these findings might not always be realistic in physical world applications, which are often dimension- or delay-limited. In this work, the optimum ways to achieve minimum probability of error for binary signaling through a scalar modulo AWGN channel is examined under different scenarios where the receiver has access to full or partial information. In case of partial information at the receiver, an iterative estimation rule is proposed to reduce the error rate, and the performance of different estimators are demonstrated in simulated experiments.
Head-driven Phrase Structure Parsing in O($n^3$) Time Complexity
Venue: arXiv cs.CL 2021
Year: 2021

Constituent and dependency parsing, the two classic forms of syntactic parsing, have been found to benefit from joint training and decoding under a uniform formalism, Head-driven Phrase Structure Grammar (HPSG). However, decoding this unified grammar has a higher time complexity ($O(n^5)$) than decoding either form individually ($O(n^3)$) since more factors have to be considered during decoding. We thus propose an improved head scorer that helps achieve a novel performance-preserved parser in $O$($n^3$) time complexity. Furthermore, on the basis of this proposed practical HPSG parser, we investigated the strengths of HPSG-based parsing and explored the general method of training an HPSG-based parser from only a constituent or dependency annotations in a multilingual scenario. We thus present a more effective, more in-depth, and general work on HPSG parsing.
Multiple Support Recovery Using Very Few Measurements Per Sample
Venue: arXiv cs.IT 2021
Year: 2021

In the problem of multiple support recovery, we are given access to linear measurements of multiple sparse samples in $\mathbb{R}^{d}$. These samples can be partitioned into $\ell$ groups, with samples having the same support belonging to the same group. For a given budget of $m$ measurements per sample, the goal is to recover the $\ell$ underlying supports, in the absence of the knowledge of group labels. We study this problem with a focus on the measurement-constrained regime where $m$ is smaller than the support size $k$ of each sample. We design a two-step procedure that estimates the union of the underlying supports first, and then uses a spectral algorithm to estimate the individual supports. Our proposed estimator can recover the supports with $m<k$ measurements per sample, from $\tilde{O}(k^{4}\ell^{4}/m^{4})$ samples. Our guarantees hold for a general, generative model assumption on the samples and measurement matrices. We also provide results from experiments conducted on synthetic data and on the MNIST dataset.
Joint Face Image Restoration and Frontalization for Recognition
Venue: arXiv cs.CV 2021
Year: 2021

In real-world scenarios, many factors may harm face recognition performance, e.g., large pose, bad illumination,low resolution, blur and noise. To address these challenges, previous efforts usually first restore the low-quality faces to high-quality ones and then perform face recognition. However, most of these methods are stage-wise, which is sub-optimal and deviates from the reality. In this paper, we address all these challenges jointly for unconstrained face recognition. We propose an Multi-Degradation Face Restoration (MDFR) model to restore frontalized high-quality faces from the given low-quality ones under arbitrary facial poses, with three distinct novelties. First, MDFR is a well-designed encoder-decoder architecture which extracts feature representation from an input face image with arbitrary low-quality factors and restores it to a high-quality counterpart. Second, MDFR introduces a pose residual learning strategy along with a 3D-based Pose Normalization Module (PNM), which can perceive the pose gap between the input initial pose and its real-frontal pose to guide the face frontalization. Finally, MDFR can generate frontalized high-quality face images by a single unified network, showing a strong capability of preserving face identity. Qualitative and quantitative experiments on both controlled and in-the-wild benchmarks demonstrate the superiority of MDFR over state-of-the-art methods on both face frontalization and face restoration.
Happy Dance, Slow Clap: Using Reaction GIFs to Predict Induced Affect on Twitter
Venue: arXiv cs.CL 2021
Year: 2021

Datasets with induced emotion labels are scarce but of utmost importance for many NLP tasks. We present a new, automated method for collecting texts along with their induced reaction labels. The method exploits the online use of reaction GIFs, which capture complex affective states. We show how to augment the data with induced emotion and induced sentiment labels. We use our method to create and publish ReactionGIF, a first-of-its-kind affective dataset of 30K tweets. We provide baselines for three new tasks, including induced sentiment prediction and multilabel classification of induced emotions. Our method and dataset open new research opportunities in emotion detection and affective computing.
Evaluating Robustness over High Level Driving Instruction for Autonomous Driving
Venue: arXiv cs.LG 2021
Year: 2021

In recent years, we have witnessed increasingly high performance in the field of autonomous end-to-end driving. In particular, more and more research is being done on driving in urban environments, where the car has to follow high level commands to navigate. However, few evaluations are made on the ability of these agents to react in an unexpected situation. Specifically, no evaluations are conducted on the robustness of driving agents in the event of a bad high-level command. We propose here an evaluation method, namely a benchmark that allows to assess the robustness of an agent, and to appreciate its understanding of the environment through its ability to keep a safe behavior, regardless of the instruction.
ASQ: Automatically Generating Question-Answer Pairs using AMRs
Venue: arXiv cs.CL 2021
Year: 2021

We introduce ASQ, a tool to automatically mine questions and answers from a sentence using the Abstract Meaning Representation (AMR). Previous work has used question-answer pairs to specify the predicate-argument structure of a sentence using natural language, which does not require linguistic expertise or training, and created datasets such as QA-SRL and QAMR, for which the question-answer pair annotations were crowdsourced. Our goal is to build a tool (ASQ) that maps from the traditional meaning representation AMR to a question-answer meaning representation (QMR). This enables construction of QMR datasets automatically in various domains using existing high-quality AMR parsers, and provides an automatic mapping AMR to QMR for ease of understanding by non-experts. A qualitative evaluation of the output generated by ASQ from the AMR 2.0 data shows that the question-answer pairs are natural and valid, and demonstrate good coverage of the content. We run ASQ on the sentences from the QAMR dataset, to observe that the semantic roles in QAMR are also captured by ASQ. We intend to make this tool and the results publicly available for others to use and build upon.
Generalized smoothed particle hydrodynamics with overset methods in total Lagrangian formulations
Venue: arXiv cs.CE 2021
Year: 2021

This study proposes a generalized coordinates based smoothed particle hydrodynamics (GSPH) method with overset methods using a Total Lagrangian (TL) formulation for large deformation and crack propagation problems. In the proposed GSPH, the physical space is decomposed into multiple domains, each of which is mapped to a local coordinate space (generalized space) to avoid coordinate singularities as well as to flexibly change the spatial resolution. The smoothed particle hydrodynamics (SPH) particles are then non-uniformly, e.g., typically in the boundary-conforming way, distributed in the physical space while they are defined uniformly in each generalized space similarly to the normal SPH method, which are numerically related by a coordinate transformation matrix. By solving a governing equation in each generalized space, the shape and size of the SPH kernel can be spatially changed in the physical space so that a spatial resolution is adaptively varied a priori depending on the deformation characteristics, and thus, a low-cost calculation with the less number of particles is achieved in complex shape structures.
Data Curation and Quality Assurance for Machine Learning-based Cyber Intrusion Detection
Venue: arXiv cs.CR 2021
Year: 2021

Intrusion detection is an essential task in the cyber threat environment. Machine learning and deep learning techniques have been applied for intrusion detection. However, most of the existing research focuses on the model work but ignores the fact that poor data quality has a direct impact on the performance of a machine learning system. More attention should be paid to the data work when building a machine learning-based intrusion detection system. This article first summarizes existing machine learning-based intrusion detection systems and the datasets used for building these systems. Then the data preparation workflow and quality requirements for intrusion detection are discussed. To figure out how data and models affect machine learning performance, we conducted experiments on 11 HIDS datasets using seven machine learning models and three deep learning models. The experimental results show that BERT and GPT were the best algorithms for HIDS on all of the datasets. However, the performance on different datasets varies, indicating the differences between the data quality of these datasets. We then evaluate the data quality of the 11 datasets based on quality dimensions proposed in this paper to determine the best characteristics that a HIDS dataset should possess in order to yield the best possible result. This research initiates a data quality perspective for researchers and practitioners to improve the performance of machine learning-based intrusion detection.
A Review on Trust in Human-Robot Interaction
Venue: arXiv cs.RO 2021
Year: 2021

Due to agile developments in the field of robotics and human-robot interaction, prospective robotic agents are intended to play the role of teammates and partner with humans to perform operations, rather than tools that are replacing humans helping humans in a specific task. this notion of partnering with robots raises new challenges for human-robot interaction (HRI), which gives rise to a new field of research in HRI, namely human-robot trust. Where humans and robots are working as partners, the performance of the work can be diminished if humans do not trust robots appropriately. Considering the impact of human-robot trust observed in different HRI fields, many researchers have investigated the field of human-robot trust and examined various concerns related to human-robot trust. In this work, we review the past works on human-robot trust based on the research topics and discuss selected trends in this field. Based on these reviews, we finally propose some ideas and areas of potential future research at the end of this paper.